1. Field
Embodiments of the invention relate to iterative refinement of search results based on user feedback.
2. Description of the Related Art
A user may issue a query (e.g., using one or more query terms) to a search engine. The search engine attempts to identify documents that the user may be interested in based on the query terms. Then, the search engine returns a list of documents to the user as search results, and the search results are displayed on one or more pages for the user.
Users' actions in reviewing search results can be used in understanding their preferences. For example, in some conventional systems, users, after reading search results, can express interest in certain results, by clicking through to them or by clicking a special User Interface (UI) feature for the purpose, and, at the same time, the users can ignore other search results. The user input can be used to improve the relevance of search results in refining the current query or subsequent queries.
Some conventional techniques automatically learn relevance from past search activities, and apply this learning to future search activities. For example, a conventional technique may use learning algorithms to scope a user's interests and use this as feedback into a result-ranking algorithm by assigning extra weight to results that are most likely to be relevant to the user. One limitation of this approach is in requiring the maintenance of a multi-session history for all queries, including persistence of queries in other semantic areas that may be completely irrelevant to the current search. A session may be described as a period associated with one user's log-in. For example, launching an instance of the browser and executing queries for a user is associated with one session (e.g., the launching and execution occur during the session). Once the browser is closed, the session is closed.
Other conventional techniques make use of implicit or explicit feedback in order to generate additional queries, in order to return more accurate results. These conventional techniques attempt to improve the search results relevance within a single search session. For example, some conventional techniques modify the original queries to include information extracted after applying information retrieval techniques to a set of relevant and irrelevant documents. However, automatically refined subsequent queries may be very noisy, especially when a small amount of feedback is available. In certain cases, the automatically refined subsequent queries may return results that are actually less relevant than those returned in the original query. For example, “noisy” may describe that the original query is amended by leveraging the locale of the browser or the language of the browser. This is good as long as the user understands the reason behind narrowing of the queries. However, if the user travels to a different geography, the user may not be able to find documents relevant to the new location as the query was amended for the prior locale.